SOTAVerified

Multi-class Classification

Multi-class classification is a type of supervised learning where the goal is to assign an input to one of three or more distinct classes. Unlike binary classification (which has only two classes), multi-class classification handles multiple labels and uses algorithms like logistic regression, decision trees, random forests, SVMs, or neural networks to predict the correct category based on the features of the input data.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 851–875 of 903 papers

TitleStatusHype
Faint Features Tell: Automatic Vertebrae Fracture Screening Assisted by Contrastive Learning—0
Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression—0
Fast Recursive Multi-class Classification of Pairs of Text Entities for Biomedical Event Extraction—0
Fast Training of Effective Multi-class Boosting Using Coordinate Descent Optimization—0
FD-LLM: Large Language Model for Fault Diagnosis of Machines—0
Feature-aware conditional GAN for category text generation—0
Feature Incay for Representation Regularization—0
FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning—0
Cross-System Categorization of Abnormal Traces in Microservice-Based Systems via Meta-Learning—0
Filtering Aggression from the Multilingual Social Media Feed—0
Fine-grained Generalization Analysis of Vector-valued Learning—0
Fine-tuning Large Language Models for Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection—0
FingerNet: EEG Decoding of A Fine Motor Imagery with Finger-tapping Task Based on A Deep Neural Network—0
Fixing Asymptotic Uncertainty of Bayesian Neural Networks with Infinite ReLU Features—0
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence—0
FLAG: Fast Label-Adaptive Aggregation for Multi-label Classification in Federated Learning—0
Flat and Nested Negation and Uncertainty Detection with PubMed BERT—0
"Flux+Mutability": A Conditional Generative Approach to One-Class Classification and Anomaly Detection—0
FOLD-SE: An Efficient Rule-based Machine Learning Algorithm with Scalable Explainability—0
FoLDTree: A ULDA-Based Decision Tree Framework for Efficient Oblique Splits and Feature Selection—0
FORML: Learning to Reweight Data for Fairness—0
Gaussian Processes on Hypergraphs—0
Generalization Analysis on Learning with a Concurrent Verifier—0
Generalization and Risk Bounds for Recurrent Neural Networks—0
Generalized Conditional Gradient for Sparse Estimation—0
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All datasetsCOVID-19 CXR DatasetCOVID chest X-rayReuters-52TII-SSRC-23Training and validation dataset of capsule vision 2024 challenge.

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1COVID-CXNetAccuracy (%)94.2—Unverified
#ModelMetricClaimedVerifiedStatus
1COVID-ResNetF1 score0.9—Unverified
#ModelMetricClaimedVerifiedStatus
1SVM (tficf)Macro F173.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified